16 August 2001 Multiple-model nonlinear filtering for low-signal ground target applications
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Abstract
This paper describes the design and implementation of multiple model nonlinear filters (MMNLF) for ground target tracking using Ground Moving Target Indicator (GMTI) radar measurements. The MMNLF is based on a general theory of hybrid continuous-discrete dynamics. The motion model state is discrete and its stochastic dynamics are a continuous- time Markov chain. For each motion model, the continuum dynamics are a continuous-state Markov process described here by appropriate Fokker-Plank equations. This is illustrated here by a specific two-model MMNLF in which one motion model incorporates terrain, road, and vehicle motion constraints derived from battlefield observations. The second model is slow diffusion in speed and heading. The target state conditional probability density is discretized on a moving grid and recursively updated with sensor measurements via Bayes' formula. The conditional density is time updated between sensor measurements using Alternating Direction Implicit (ADI) finite difference methods. In simulation testing against low signal to clutter + noise Ratio (SNCR) targets, the MMNLF is able to maintain track in situations where single model filters based on either of the component models fail. Potential applications of this work include detection and tracking of foliage-obscured moving targets.
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Chris M. Kreucher, Chris M. Kreucher, Keith D. Kastella, Keith D. Kastella, } "Multiple-model nonlinear filtering for low-signal ground target applications", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); doi: 10.1117/12.436946; https://doi.org/10.1117/12.436946
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